A Convolutional Neural Network Pipeline For Multi-Temporal Retinal Image Registration

Ho, Chi-Jui; Wang, Yiqian; Zhang, Junkang; Nguyen, Truong Q.; An, Cheolhong

doi:10.1109/isocc53507.2021.9613906

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Thus, registration is an essential part of medical diagnoses that depend on imaging technologies. IR was applied in retina imaging (Ho et al, 2021), breast imaging (Ringel et al, (Ho et al, 2021) Unimodal Uni 1-to-1 Coarse-f. Unsupervised Eye (Hou et al, 2022) Unimodal PET Uni 1-to-1 1 Unsupervised Heart (Kujur et al, 2022) Multimodal MR Uni 1-to-1 1 Classical Brain (Lee et al, 2022) Unimodal CT Uni 1-to-1 1 Supervised Kidney (Li et al, 2022) Unimodal MR Uni 1-to-1 1 Unsupervised Brain (Liu et al, 2021) Unimodal Uni 1-to-1 1 Classical Tissues (Ma et al, 2022) Unimodal MR Uni 1-to-1 1 Unsupervised Brain (Maillard et al, 2022) Unimodal MR Uni Metamorphic m:n 1 Neuro-symbolic Brain (Meng et al, 2022) Unimodal MR Uni 1-to-1 1 Unsupervised Brain (Mok et al, 2022) Unimodal MR Uni 1-to-1 Coarse-f. Unsupervised Brain (Naik et al, 2022) Multimodal CT-Xray Uni 1-to-1 Coarse-f. Classical Spine (Nazib et al, 2021) Unimodal MR Bi 1-to-1 1 Unsupervised Brain (Park et al, 2022) Unimodal CT/MR Uni 1-to-1 1 Unsupervised (Ringel et al, 2022) Unimodal MR Uni 1-to-1 1 Classical Breast (Robertson et al, 2022) Multimodal CT/MRvideo 1-to-1 Coarse-f. Software Head (Saadat et al, 2022) Multimodal…”

Section: Medical Applicationsmentioning

confidence: 99%

Deep Learning in Medical Image Registration: Introduction and Survey

Hammoudeh,

Dupont

2023

Preprint

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Image registration (IR) is a process that deforms images to align them with respect to a reference space, making it easier for medical practitioners to examine various medical images in a standardized reference frame, such as having the same rotation and scale. This document introduces image registration using a simple numeric example. It provides a definition of image registration along with a space-oriented symbolic representation. This review covers various aspects of image transformations, including affine, deformable, invertible, and bidirectional transformations, as well as medical image registration algorithms such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It also explores atlas-based registration and multistage image registration techniques, including coarse-fine and pyramid approaches. Furthermore, this survey paper discusses medical image registration taxonomies, datasets, evaluation measures, such as correlation-based metrics, segmentation-based metrics, processing time, and model size. It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis. Finally, the document addresses future research directions, including the further development of transformers.

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Section: Medical Applicationsmentioning

confidence: 99%

Deep Learning in Medical Image Registration: Introduction and Survey

Hammoudeh,

Dupont

2023

Preprint

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“…The accuracy of the proposed tool wear state recognition method was above 90%. Ho et al [13] defined a new direction for the monitoring of the machining process using machine learning tools to analyze recorded data during machining, such as cutting force, acceleration, sound, image, etc.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Roughness, the Material Removal Rate, and the Acoustic Emission Signal Obtained in Flat Grinding Processes

Sender,

Buj-Corral,

Álvarez-Flórez

2024

Machines

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In this work, the analysis of the acoustic emission (AE) signal in grinding processes is addressed. The proposed analysis method decomposes the acoustic signal into three frequency ranges. The total energy of each range is determined, as well as the highest frequency. Different grinding experiments were carried out, according to a full factorial design of experiments (DOE), in which feed speed, depth of cut, and transversal step (table cross feed) were varied. Arithmetic average roughness Ra and the material removal rate (MRR) were determined. It was observed that Ra depends mainly on the transversal step, followed by feed speed and the interaction between the transversal step and depth of cut, while MRR is greatly influenced by the transversal step. According to multi-objective optimization with the Derringer–Suich function, in order to simultaneously minimize Ra and maximize MRR, a transversal step of 9 mm per longitudinal pass, feed speed of 20 m/min, and depth of cut of 0.020 mm should be selected.

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A Convolutional Neural Network Pipeline For Multi-Temporal Retinal Image Registration

Cited by 2 publications

References 5 publications

Deep Learning in Medical Image Registration: Introduction and Survey

Deep Learning in Medical Image Registration: Introduction and Survey

Analysis of Roughness, the Material Removal Rate, and the Acoustic Emission Signal Obtained in Flat Grinding Processes

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